The 24-bit RGB color space is commonly used in many display systems such as monitor, television etc. In order to be displayed on a 24-bit RGB display, images resulting from a higher precision capturing or processing system have to be first quantized to 3×8 bit RGB true color signals. In the past, this 24-bit color space is thought to be more than enough for color representation. However, as display technology advances and brightness level increases, consumers are no longer satisfied with existing 24-bit color displays.
Higher bit-depth displays, including the higher bit processing chips and drivers, are becoming a trend in the display industry. Still, most of the existing displays and the displays to be produced in the near future are 8-bits per channel. Representing color data with more than 8-bits per channel using these 8-bit displays and maintaining the video quality at the same time is highly desirable.
Attempts at using less bit images to represent more bit images have been around in printing community. Halftoning algorithms are used to transform continuous-tone images to binary images in order to be printed by either a laser or inkjet printer. Two categories of halftoning methods are primarily used: dithering and error diffusion. Both methods capitalize on the low pass characteristic of the human visual system, and redistribute quantization errors to the high frequencies which are less noticeable to a human viewer. The major difference between dithering and error diffusion is that dithering operates pixel-by-pixel based on the pixel's coordinate, and error diffusion algorithm operates based on a running error. Hardware implementation of halftoning by error diffusion requires more memory than by dithering.
Halftoning algorithms developed for printing can be used in representing more bit depth video using 8-bit video displays. In general, spatial dithering is applied to video quantization because it is both simple and fast. However, for video displays, the temporal dimension (time) makes it possible to exploit the human visual system's integration in the temporal domain to increase the precision of a color to be represented. One way of doing so is to generalize the existing two-dimensional dithering methods to three-dimensional spatiotemporal dithering, which includes using a three-dimensional dithering mask and combining a two dimensional spatial dithering algorithm with a temporal error diffusion. Also, error diffusion algorithms can be directly generalized to three dimensional with a three dimensional diffusion filter. These methods simply extend the two-dimensional halftoning methods to three-dimensional, and do not consider the temporal properties of human vision system. In addition, the methods with temporal error diffusion need frame memory which is expensive in hardware implementation.